Data Interpretation and Video Games Sales Prediction Using Machine Learning Algorithms- a Comparative Study

In today’s world, Video game industry has developed immensely that it fascinates people to a great extent. It is proved to be a noticeable and important contributor to the entire world in terms of revenue. This field has a greater influence, without any doubt on the population. It has extraordinarily attracted many people with sharp and creative skills to expand and enlarge Video games globally. Discussing about the vast revenue generated by the gaming industry, Machine learning technologies is widely used to develop models that is highly effective in examining and anticipating the sales of the computer games well in advance. Machine Learning mechanism comes up with a good deal of models to envision the future sales with the help of Linear, Multiple Regression, Random Forest, Decision Trees, Support Vector Machines and more. While all these handles the data by using different mathematical concepts and formulae to estimate the sales, any or the best model could be selected for the appropriate data by comparing their accuracy and their performance. The model accuracy measure is taken by total number of correct predictions evaluated by the respective model in comparison with all the predictions. Hence, r-square is extensively used as a performance metric to grade the working of the model. In particular, four algorithms are tested upon the chosen dataset and finally compared to detect the finest working model.

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